Automate Data Pipelines: Schema Evolution

Automate Data Pipelines: Schema Evolution is an intermediate course designed for data engineers, analysts, and developers looking to build robust, failure-resistant data workflows. In today's dynamic data landscape, pipelines often break when source data structures change unexpectedly—a problem known as schema drift. This course tackles that challenge head-on, teaching you how to design and automate data pipelines that can gracefully handle schema evolution using Apache Airflow. You will gain hands-on experience designing, building, and scheduling complex data pipelines (DAGs) that automate ETL processes from extraction to loading. The curriculum places a strong emphasis on creating idempotent workflows that detect and adapt to schema changes, ensuring data integrity and preventing costly failures. Through practical labs and real-world case studies from companies like Uber and BharatPe, you will implement data validation checks and build comprehensive monitoring and alerting systems. By the end of this course, you will be equipped to create resilient, scalable, and fully automated data pipelines that are built to withstand the complexities of real-world data environments

• Build automated data pipelines with Apache Airflow
• Manage schema evolution to prevent failures
• Implement continuous monitoring for data integrity
• Create idempotent workflows that detect schema changes

• Basic familiarity with topic and terminology
• Willingness to engage in applied exercises or case studies